





import copy
import fnmatch
import json
import os
import pickle as pkl
import shutil
import sys
import tarfile
import tempfile
from collections import OrderedDict
from contextlib import contextmanager
from functools import partial
from hashlib import shaaaa
from io import BytesIO
from pathlib import Path
from urllib.parse import urlparse
from zipfile import ZipFile, is_zipfile

import cva
import numpy as np
import requests
import wget
from filelock import FileLock
from PIL import Image
from tqdm.auto import tqdm
from yaml import Loader, dump, load


try:
  import torch

  _A					:				Optional[int]				=			True
except ImportError:
  _A					:				List[str]				=			False


try:
  from torch.hub import _get_torch_home

  _A					:				List[Any]				=			_get_torch_home()
except ImportError:
  _A					:				int				=			os.path.expanduser(
      os.getenv("""TORCH_HOME""", os.path.join(os.getenv("""XDG_CACHE_HOME""", """~/.cache"""), """torch"""))
  )

_A					:				int				=			os.path.join(torch_cache_home, """transformers""")

_A					:				str				=			"""https://cdn.huggingface.co"""
_A					:				List[str]				=			"""https://s3.amazonaws.com/models.huggingface.co/bert"""
_A					:				Dict				=			"""/""".join(str(Path(__file__).resolve()).split("""/""")[:-1])
_A					:				Optional[Any]				=			os.path.join(PATH, """config.yaml""")
_A					:				List[Any]				=			os.path.join(PATH, """attributes.txt""")
_A					:				List[Any]				=			os.path.join(PATH, """objects.txt""")
_A					:				str				=			os.getenv("""PYTORCH_PRETRAINED_BERT_CACHE""", default_cache_path)
_A					:				List[Any]				=			os.getenv("""PYTORCH_TRANSFORMERS_CACHE""", PYTORCH_PRETRAINED_BERT_CACHE)
_A					:				List[Any]				=			os.getenv("""TRANSFORMERS_CACHE""", PYTORCH_TRANSFORMERS_CACHE)
_A					:				Tuple				=			"""pytorch_model.bin"""
_A					:				Any				=			"""config.yaml"""


def 							__snake_case							(    lowerCAmelCase_=OBJECTS ,							lowerCAmelCase_=ATTRIBUTES	)				->						Any:
    SCREAMING_SNAKE_CASE__											=			[]
    with open(lowerCAmelCase_	) as f:
        for object in f.readlines():
            vg_classes.append(object.split(''','''	)[0].lower().strip()	)

    SCREAMING_SNAKE_CASE__											=			[]
    with open(lowerCAmelCase_	) as f:
        for object in f.readlines():
            vg_attrs.append(object.split(''','''	)[0].lower().strip()	)
    return vg_classes, vg_attrs


def 							__snake_case							(    lowerCAmelCase_	)				->						List[Any]:
    SCREAMING_SNAKE_CASE__											=			OrderedDict()
    with open(lowerCAmelCase_ ,							'''rb'''	) as f:
        SCREAMING_SNAKE_CASE__											=			pkl.load(lowerCAmelCase_	)['''model''']
    for k in copy.deepcopy(list(ckp.keys()	)	):
        SCREAMING_SNAKE_CASE__											=			ckp.pop(lowerCAmelCase_	)
        if isinstance(lowerCAmelCase_ ,							np.ndarray	):
            SCREAMING_SNAKE_CASE__											=			torch.tensor(lowerCAmelCase_	)
        else:
            assert isinstance(lowerCAmelCase_ ,							torch.tensor	), type(lowerCAmelCase_	)
        SCREAMING_SNAKE_CASE__											=			v
    return r



class 					__snake_case	:


    '''simple docstring'''







    lowerCamelCase__    :		int				=	{}



    def __init__(       self   ,				A_   ,				A_ = "root"   ,				A_=0     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			name
        SCREAMING_SNAKE_CASE__											=			level
        SCREAMING_SNAKE_CASE__											=			{}
        for k, v in dictionary.items():
            if v is None:
                raise ValueError()
            SCREAMING_SNAKE_CASE__											=			copy.deepcopy(A_     )
            SCREAMING_SNAKE_CASE__											=			copy.deepcopy(A_     )
            if isinstance(A_   ,				A_     ):
                SCREAMING_SNAKE_CASE__											=			Config(A_   ,				name=A_   ,				level=level + 1     )
            SCREAMING_SNAKE_CASE__											=			v
            setattr(self   ,				A_   ,				A_     )

        SCREAMING_SNAKE_CASE__											=			d



    def __repr__(       self     ):





        '''simple docstring'''

        return str(list((self._pointer.keys())     )     )



    def __setattr__(       self   ,				A_   ,				A_     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			val
        SCREAMING_SNAKE_CASE__											=			val
        SCREAMING_SNAKE_CASE__											=			key.split('''.'''     )
        SCREAMING_SNAKE_CASE__											=			len(A_     ) - 1
        SCREAMING_SNAKE_CASE__											=			self._pointer
        if len(A_     ) > 1:
            for i, l in enumerate(A_     ):
                if hasattr(self   ,				A_     ) and isinstance(getattr(self   ,				A_     )   ,				A_     ):
                    setattr(getattr(self   ,				A_     )   ,				'''.'''.join(levels[i:]     )   ,				A_     )
                if l == last_level:
                    SCREAMING_SNAKE_CASE__											=			val
                else:
                    SCREAMING_SNAKE_CASE__											=			pointer[l]



    def  lowercase_					(       self     ):





        '''simple docstring'''

        return self._pointer



    def  lowercase_					(       self   ,				A_   ,				A_     ):





        '''simple docstring'''

        with open(f'''{file_name}'''   ,				'''w'''     ) as stream:
            dump(A_   ,				A_     )



    def  lowercase_					(       self   ,				A_   ,				A_     ):





        '''simple docstring'''

        with open(f'''{file_name}'''   ,				'''w'''     ) as stream:
            json.dump(A_   ,				A_     )



    @staticmethod
    def  lowercase_					(       A_     ):





        '''simple docstring'''

        with open(A_     ) as stream:
            SCREAMING_SNAKE_CASE__											=			load(A_   ,				Loader=A_     )
        return data



    def __str__(       self     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			'''    '''
        if self._name != "root":
            SCREAMING_SNAKE_CASE__											=			f'''{t * (self._level-1)}{self._name}:\n'''
        else:
            SCREAMING_SNAKE_CASE__											=			''''''
        SCREAMING_SNAKE_CASE__											=			self._level
        for i, (k, v) in enumerate(self._pointer.items()     ):
            if isinstance(A_   ,				A_     ):
                r += f'''{t * (self._level)}{v}\n'''
                self._level += 1
            else:
                r += f'''{t * (self._level)}{k}: {v} ({type(A_     ).__name__})\n'''
            SCREAMING_SNAKE_CASE__											=			level
        return r[:-1]



    @classmethod
    def  lowercase_					(       cls   ,				A_   ,				**A_     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__											=			cls.get_config_dict(A_   ,				**A_     )
        return cls(A_     )




    @classmethod
    def  lowercase_					(       cls   ,				A_   ,				**A_     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			kwargs.pop('''cache_dir'''   ,				A_     )
        SCREAMING_SNAKE_CASE__											=			kwargs.pop('''force_download'''   ,				A_     )
        SCREAMING_SNAKE_CASE__											=			kwargs.pop('''resume_download'''   ,				A_     )
        SCREAMING_SNAKE_CASE__											=			kwargs.pop('''proxies'''   ,				A_     )
        SCREAMING_SNAKE_CASE__											=			kwargs.pop('''local_files_only'''   ,				A_     )

        if os.path.isdir(A_     ):
            SCREAMING_SNAKE_CASE__											=			os.path.join(A_   ,				A_     )
        elif os.path.isfile(A_     ) or is_remote_url(A_     ):
            SCREAMING_SNAKE_CASE__											=			pretrained_model_name_or_path
        else:
            SCREAMING_SNAKE_CASE__											=			hf_bucket_url(A_   ,				filename=A_   ,				use_cdn=A_     )

        try:
            # Load from URL or cache if already cached
            SCREAMING_SNAKE_CASE__											=			cached_path(
                A_   ,				cache_dir=A_   ,				force_download=A_   ,				proxies=A_   ,				resume_download=A_   ,				local_files_only=A_   ,				)
            # Load config dict
            if resolved_config_file is None:
                raise EnvironmentError

            SCREAMING_SNAKE_CASE__											=			Config.load_yaml(A_     )

        except EnvironmentError:
            SCREAMING_SNAKE_CASE__											=			'''Can\'t load config for'''
            raise EnvironmentError(A_     )

        if resolved_config_file == config_file:
            print('''loading configuration file from path'''     )
        else:
            print('''loading configuration file cache'''     )

        return Config.load_yaml(A_     ), kwargs





def 							__snake_case							(    lowerCAmelCase_	)				->						Tuple:
    SCREAMING_SNAKE_CASE__											=			torch.load('''dump.pt''' ,							map_location=in_tensor.device	)
    SCREAMING_SNAKE_CASE__											=			in_tensor.numpy()
    SCREAMING_SNAKE_CASE__											=			out_tensor.numpy()[0]
    print(na.shape ,							na[0, 0, :5]	)
    print(na.shape ,							na[0, 0, :5]	)
    assert np.allclose(lowerCAmelCase_ ,							lowerCAmelCase_ ,							rtol=0.01 ,							atol=0.1	), (
        f'''{sum([1 for x in np.isclose(lowerCAmelCase_ ,							lowerCAmelCase_ ,							rtol=0.01 ,							atol=0.1	).flatten() if x is False]	)/len(na.flatten()	)*1_0_0:.4f} %'''
        " element-wise mismatch"
    )
    raise Exception('''tensors are all good'''	)

    # Hugging face functions below


def 							__snake_case							(    lowerCAmelCase_	)				->						str:
    SCREAMING_SNAKE_CASE__											=			urlparse(lowerCAmelCase_	)
    return parsed.scheme in ("http", "https")


def 							__snake_case							(    lowerCAmelCase_ ,							lowerCAmelCase_ ,							lowerCAmelCase_=True	)				->						str:
    SCREAMING_SNAKE_CASE__											=			CLOUDFRONT_DISTRIB_PREFIX if use_cdn else S3_BUCKET_PREFIX
    SCREAMING_SNAKE_CASE__											=			'''/''' not in model_id
    if legacy_format:
        return f'''{endpoint}/{model_id}-{filename}'''
    else:
        return f'''{endpoint}/{model_id}/{filename}'''


def 							__snake_case							(    lowerCAmelCase_ ,							lowerCAmelCase_ ,							lowerCAmelCase_=None ,							lowerCAmelCase_=0 ,							lowerCAmelCase_=None ,							)				->						Any:
    SCREAMING_SNAKE_CASE__											=			'''python/{}'''.format(sys.version.split()[0]	)
    if _torch_available:
        ua += "; torch/{}".format(torch.__version__	)
    if isinstance(lowerCAmelCase_ ,							lowerCAmelCase_	):
        ua += "; " + "; ".join('''{}/{}'''.format(lowerCAmelCase_ ,							lowerCAmelCase_	) for k, v in user_agent.items()	)
    elif isinstance(lowerCAmelCase_ ,							lowerCAmelCase_	):
        ua += "; " + user_agent
    SCREAMING_SNAKE_CASE__											=			{'''user-agent''': ua}
    if resume_size > 0:
        SCREAMING_SNAKE_CASE__											=			'''bytes=%d-''' % (resume_size,)
    SCREAMING_SNAKE_CASE__											=			requests.get(lowerCAmelCase_ ,							stream=lowerCAmelCase_ ,							proxies=lowerCAmelCase_ ,							headers=lowerCAmelCase_	)
    if response.status_code == 4_1_6:  # Range not satisfiable
        return
    SCREAMING_SNAKE_CASE__											=			response.headers.get('''Content-Length'''	)
    SCREAMING_SNAKE_CASE__											=			resume_size + int(lowerCAmelCase_	) if content_length is not None else None
    SCREAMING_SNAKE_CASE__											=			tqdm(
        unit='''B''' ,							unit_scale=lowerCAmelCase_ ,							total=lowerCAmelCase_ ,							initial=lowerCAmelCase_ ,							desc='''Downloading''' ,							)
    for chunk in response.iter_content(chunk_size=1_0_2_4	):
        if chunk:  # filter out keep-alive new chunks
            progress.update(len(lowerCAmelCase_	)	)
            temp_file.write(lowerCAmelCase_	)
    progress.close()


def 							__snake_case							(    lowerCAmelCase_ ,							lowerCAmelCase_=None ,							lowerCAmelCase_=False ,							lowerCAmelCase_=None ,							lowerCAmelCase_=1_0 ,							lowerCAmelCase_=False ,							lowerCAmelCase_=None ,							lowerCAmelCase_=False ,							)				->						List[Any]:
    if cache_dir is None:
        SCREAMING_SNAKE_CASE__											=			TRANSFORMERS_CACHE
    if isinstance(lowerCAmelCase_ ,							lowerCAmelCase_	):
        SCREAMING_SNAKE_CASE__											=			str(lowerCAmelCase_	)

    os.makedirs(lowerCAmelCase_ ,							exist_ok=lowerCAmelCase_	)

    SCREAMING_SNAKE_CASE__											=			None
    if not local_files_only:
        try:
            SCREAMING_SNAKE_CASE__											=			requests.head(lowerCAmelCase_ ,							allow_redirects=lowerCAmelCase_ ,							proxies=lowerCAmelCase_ ,							timeout=lowerCAmelCase_	)
            if response.status_code == 2_0_0:
                SCREAMING_SNAKE_CASE__											=			response.headers.get('''ETag'''	)
        except (EnvironmentError, requests.exceptions.Timeout):
            # etag is already None
            pass

    SCREAMING_SNAKE_CASE__											=			url_to_filename(lowerCAmelCase_ ,							lowerCAmelCase_	)

    # get cache path to put the file
    SCREAMING_SNAKE_CASE__											=			os.path.join(lowerCAmelCase_ ,							lowerCAmelCase_	)

    # etag is None = we don't have a connection, or url doesn't exist, or is otherwise inaccessible.
    # try to get the last downloaded one
    if etag is None:
        if os.path.exists(lowerCAmelCase_	):
            return cache_path
        else:
            SCREAMING_SNAKE_CASE__											=			[
                file
                for file in fnmatch.filter(os.listdir(lowerCAmelCase_	) ,							filename + '''.*'''	)
                if not file.endswith('''.json'''	) and not file.endswith('''.lock'''	)
            ]
            if len(lowerCAmelCase_	) > 0:
                return os.path.join(lowerCAmelCase_ ,							matching_files[-1]	)
            else:
                # If files cannot be found and local_files_only=True,
                # the models might've been found if local_files_only=False
                # Notify the user about that
                if local_files_only:
                    raise ValueError(
                        '''Cannot find the requested files in the cached path and outgoing traffic has been'''
                        ''' disabled. To enable model look-ups and downloads online, set \'local_files_only\''''
                        ''' to False.'''	)
                return None

    # From now on, etag is not None.
    if os.path.exists(lowerCAmelCase_	) and not force_download:
        return cache_path

    # Prevent parallel downloads of the same file with a lock.
    SCREAMING_SNAKE_CASE__											=			cache_path + '''.lock'''
    with FileLock(lowerCAmelCase_	):
        # If the download just completed while the lock was activated.
        if os.path.exists(lowerCAmelCase_	) and not force_download:
            # Even if returning early like here, the lock will be released.
            return cache_path

        if resume_download:
            SCREAMING_SNAKE_CASE__											=			cache_path + '''.incomplete'''

            @contextmanager
            def _resumable_file_manager():
                with open(lowerCAmelCase_ ,							'''a+b'''	) as f:
                    yield f

            SCREAMING_SNAKE_CASE__											=			_resumable_file_manager
            if os.path.exists(lowerCAmelCase_	):
                SCREAMING_SNAKE_CASE__											=			os.stat(lowerCAmelCase_	).st_size
            else:
                SCREAMING_SNAKE_CASE__											=			0
        else:
            SCREAMING_SNAKE_CASE__											=			partial(tempfile.NamedTemporaryFile ,							dir=lowerCAmelCase_ ,							delete=lowerCAmelCase_	)
            SCREAMING_SNAKE_CASE__											=			0

        # Download to temporary file, then copy to cache dir once finished.
        # Otherwise you get corrupt cache entries if the download gets interrupted.
        with temp_file_manager() as temp_file:
            print(
                '''%s not found in cache or force_download set to True, downloading to %s''' ,							lowerCAmelCase_ ,							temp_file.name ,							)

            http_get(
                lowerCAmelCase_ ,							lowerCAmelCase_ ,							proxies=lowerCAmelCase_ ,							resume_size=lowerCAmelCase_ ,							user_agent=lowerCAmelCase_ ,							)

        os.replace(temp_file.name ,							lowerCAmelCase_	)

        SCREAMING_SNAKE_CASE__											=			{'''url''': url, '''etag''': etag}
        SCREAMING_SNAKE_CASE__											=			cache_path + '''.json'''
        with open(lowerCAmelCase_ ,							'''w'''	) as meta_file:
            json.dump(lowerCAmelCase_ ,							lowerCAmelCase_	)

    return cache_path


def 							__snake_case							(    lowerCAmelCase_ ,							lowerCAmelCase_=None	)				->						str:
    SCREAMING_SNAKE_CASE__											=			url.encode('''utf-8'''	)
    SCREAMING_SNAKE_CASE__											=			shaaaa(lowerCAmelCase_	)
    SCREAMING_SNAKE_CASE__											=			url_hash.hexdigest()

    if etag:
        SCREAMING_SNAKE_CASE__											=			etag.encode('''utf-8'''	)
        SCREAMING_SNAKE_CASE__											=			shaaaa(lowerCAmelCase_	)
        filename += "." + etag_hash.hexdigest()

    if url.endswith('''.h5'''	):
        filename += ".h5"

    return filename


def 							__snake_case							(    lowerCAmelCase_ ,							lowerCAmelCase_=None ,							lowerCAmelCase_=False ,							lowerCAmelCase_=None ,							lowerCAmelCase_=False ,							lowerCAmelCase_=None ,							lowerCAmelCase_=False ,							lowerCAmelCase_=False ,							lowerCAmelCase_=False ,							)				->						Optional[Any]:
    if cache_dir is None:
        SCREAMING_SNAKE_CASE__											=			TRANSFORMERS_CACHE
    if isinstance(lowerCAmelCase_ ,							lowerCAmelCase_	):
        SCREAMING_SNAKE_CASE__											=			str(lowerCAmelCase_	)
    if isinstance(lowerCAmelCase_ ,							lowerCAmelCase_	):
        SCREAMING_SNAKE_CASE__											=			str(lowerCAmelCase_	)

    if is_remote_url(lowerCAmelCase_	):
        # URL, so get it from the cache (downloading if necessary)
        SCREAMING_SNAKE_CASE__											=			get_from_cache(
            lowerCAmelCase_ ,							cache_dir=lowerCAmelCase_ ,							force_download=lowerCAmelCase_ ,							proxies=lowerCAmelCase_ ,							resume_download=lowerCAmelCase_ ,							user_agent=lowerCAmelCase_ ,							local_files_only=lowerCAmelCase_ ,							)
    elif os.path.exists(lowerCAmelCase_	):
        # File, and it exists.
        SCREAMING_SNAKE_CASE__											=			url_or_filename
    elif urlparse(lowerCAmelCase_	).scheme == "":
        # File, but it doesn't exist.
        raise EnvironmentError('''file {} not found'''.format(lowerCAmelCase_	)	)
    else:
        # Something unknown
        raise ValueError('''unable to parse {} as a URL or as a local path'''.format(lowerCAmelCase_	)	)

    if extract_compressed_file:
        if not is_zipfile(lowerCAmelCase_	) and not tarfile.is_tarfile(lowerCAmelCase_	):
            return output_path

        # Path where we extract compressed archives
        # We avoid '.' in dir name and add "-extracted" at the end: "./model.zip" => "./model-zip-extracted/"
        SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__											=			os.path.split(lowerCAmelCase_	)
        SCREAMING_SNAKE_CASE__											=			output_file.replace('''.''' ,							'''-'''	) + '''-extracted'''
        SCREAMING_SNAKE_CASE__											=			os.path.join(lowerCAmelCase_ ,							lowerCAmelCase_	)

        if os.path.isdir(lowerCAmelCase_	) and os.listdir(lowerCAmelCase_	) and not force_extract:
            return output_path_extracted

        # Prevent parallel extractions
        SCREAMING_SNAKE_CASE__											=			output_path + '''.lock'''
        with FileLock(lowerCAmelCase_	):
            shutil.rmtree(lowerCAmelCase_ ,							ignore_errors=lowerCAmelCase_	)
            os.makedirs(lowerCAmelCase_	)
            if is_zipfile(lowerCAmelCase_	):
                with ZipFile(lowerCAmelCase_ ,							'''r'''	) as zip_file:
                    zip_file.extractall(lowerCAmelCase_	)
                    zip_file.close()
            elif tarfile.is_tarfile(lowerCAmelCase_	):
                SCREAMING_SNAKE_CASE__											=			tarfile.open(lowerCAmelCase_	)
                tar_file.extractall(lowerCAmelCase_	)
                tar_file.close()
            else:
                raise EnvironmentError('''Archive format of {} could not be identified'''.format(lowerCAmelCase_	)	)

        return output_path_extracted

    return output_path


def 							__snake_case							(    lowerCAmelCase_ ,							lowerCAmelCase_=","	)				->						List[str]:
    assert isinstance(lowerCAmelCase_ ,							lowerCAmelCase_	)
    if os.path.isfile(lowerCAmelCase_	):
        with open(lowerCAmelCase_	) as f:
            SCREAMING_SNAKE_CASE__											=			eval(f.read()	)
    else:
        SCREAMING_SNAKE_CASE__											=			requests.get(lowerCAmelCase_	)
        try:
            SCREAMING_SNAKE_CASE__											=			requests.json()
        except Exception:
            SCREAMING_SNAKE_CASE__											=			req.content.decode()
            assert data is not None, "could not connect"
            try:
                SCREAMING_SNAKE_CASE__											=			eval(lowerCAmelCase_	)
            except Exception:
                SCREAMING_SNAKE_CASE__											=			data.split('''\n'''	)
        req.close()
    return data


def 							__snake_case							(    lowerCAmelCase_	)				->						Union[str, Any]:
    SCREAMING_SNAKE_CASE__											=			requests.get(lowerCAmelCase_	)
    SCREAMING_SNAKE_CASE__											=			np.array(Image.open(BytesIO(response.content	)	)	)
    return img


def 							__snake_case							(    lowerCAmelCase_	)				->						int:
    SCREAMING_SNAKE_CASE__											=			url.split('''/'''	)[-1]
    if fn not in os.listdir(os.getcwd()	):
        wget.download(lowerCAmelCase_	)
    with open(lowerCAmelCase_ ,							'''rb'''	) as stream:
        SCREAMING_SNAKE_CASE__											=			pkl.load(lowerCAmelCase_	)
    SCREAMING_SNAKE_CASE__											=			weights.pop('''model'''	)
    SCREAMING_SNAKE_CASE__											=			{}
    for k, v in model.items():
        SCREAMING_SNAKE_CASE__											=			torch.from_numpy(lowerCAmelCase_	)
        if "running_var" in k:
            SCREAMING_SNAKE_CASE__											=			torch.tensor([0]	)
            SCREAMING_SNAKE_CASE__											=			k.replace('''running_var''' ,							'''num_batches_tracked'''	)
            SCREAMING_SNAKE_CASE__											=			zero
    return new


def 							__snake_case							(    )				->						Any:
    print(f'''{os.path.abspath(os.path.join(lowerCAmelCase_ ,							os.pardir	)	)}/demo.ipynb'''	)


def 							__snake_case							(    lowerCAmelCase_ ,							lowerCAmelCase_="RGB"	)				->						int:
    assert isinstance(lowerCAmelCase_ ,							lowerCAmelCase_	)
    if os.path.isfile(lowerCAmelCase_	):
        SCREAMING_SNAKE_CASE__											=			cva.imread(lowerCAmelCase_	)
    else:
        SCREAMING_SNAKE_CASE__											=			get_image_from_url(lowerCAmelCase_	)
        assert img is not None, f'''could not connect to: {im}'''
    SCREAMING_SNAKE_CASE__											=			cva.cvtColor(lowerCAmelCase_ ,							cva.COLOR_BGR2RGB	)
    if input_format == "RGB":
        SCREAMING_SNAKE_CASE__											=			img[:, :, ::-1]
    return img



def 							__snake_case							(    lowerCAmelCase_ ,							lowerCAmelCase_=1	)				->						int:
    return (images[i : i + batch] for i in range(0 ,							len(lowerCAmelCase_	) ,							lowerCAmelCase_	))





import logging
import os
from dataclasses import dataclass
from typing import List, Optional, Union

import tqdm
from filelock import FileLock

from transformers import (
    BartTokenizer,
    BartTokenizerFast,
    DataProcessor,
    PreTrainedTokenizer,
    RobertaTokenizer,
    RobertaTokenizerFast,
    XLMRobertaTokenizer,
    is_tf_available,
    is_torch_available,
)


a      :  Optional[Any]			     =			logging.getLogger(__name__)







@dataclass(frozen=_lowerCAmelCase					)
class 				_a				:
			A      		=	42
			A      		=	42
			A      		=	None
			A      		=	None
			A      		=	None







@dataclass(frozen=_lowerCAmelCase					)
class 				_a				:
			A      		=	42
			A      		=	None
			A      		=	None
			A      		=	None
			A      		=	None


if is_torch_available():
	import torch
	from torch.utils.data import Dataset







	class 				_a				(   _lowerCAmelCase					):
				A      		=	42

				def __init__(self,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_ = None,   SCREAMING_SNAKE_CASE_=False,   SCREAMING_SNAKE_CASE_ = False,   )       ->   Union[str, Any]:
							UpperCAmelCase_:		Optional[Any]							      =     hans_processors[task]()

							UpperCAmelCase_:		Dict							      =     os.path.join(
							    SCREAMING_SNAKE_CASE_,   """cached_{}_{}_{}_{}""".format(
							        """dev""" if evaluate else """train""",   tokenizer.__class__.__name__,   str(SCREAMING_SNAKE_CASE_					),   SCREAMING_SNAKE_CASE_,   ),   )
							UpperCAmelCase_:		Dict							      =     processor.get_labels()
							if tokenizer.__class__ in (
							    RobertaTokenizer,
							    RobertaTokenizerFast,
							    XLMRobertaTokenizer,
							    BartTokenizer,
							    BartTokenizerFast,
							):
										# HACK(label indices are swapped in RoBERTa pretrained model)
										UpperCAmelCase_      ,							UpperCAmelCase_:		Tuple							      =     label_list[2], label_list[1]
							UpperCAmelCase_:		str							      =     label_list

							# Make sure only the first process in distributed training processes the dataset,
							# and the others will use the cache.
							UpperCAmelCase_:		int							      =     cached_features_file + """.lock"""
							with FileLock(SCREAMING_SNAKE_CASE_					):
										if os.path.exists(SCREAMING_SNAKE_CASE_					) and not overwrite_cache:
													logger.info(f'Loading features from cached file {cached_features_file}'					)
													UpperCAmelCase_:		Dict							      =     torch.load(SCREAMING_SNAKE_CASE_					)
										else:
													logger.info(f'Creating features from dataset file at {data_dir}'					)

													UpperCAmelCase_:		Union[str, Any]							      =     (
													    processor.get_dev_examples(SCREAMING_SNAKE_CASE_					) if evaluate else processor.get_train_examples(SCREAMING_SNAKE_CASE_					)
													)

													logger.info("""Training examples: %s""",   len(SCREAMING_SNAKE_CASE_					)					)
													UpperCAmelCase_:		List[Any]							      =     hans_convert_examples_to_features(SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_					)
													logger.info("""Saving features into cached file %s""",   SCREAMING_SNAKE_CASE_					)
													torch.save(self.features,   SCREAMING_SNAKE_CASE_					)

				def __len__(self					)       ->   Optional[int]:
							return len(self.features					)

				def __getitem__(self,   SCREAMING_SNAKE_CASE_					)       ->   InputFeatures:
							return self.features[i]

				def      __snake_case       (self					)       ->   List[str]:
							return self.label_list


if is_tf_available():
	import tensorflow as tf







	class 				_a				:
				A      		=	42

				def __init__(self,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_ = 128,   SCREAMING_SNAKE_CASE_=False,   SCREAMING_SNAKE_CASE_ = False,   )       ->   Optional[int]:
							UpperCAmelCase_:		Union[str, Any]							      =     hans_processors[task]()
							UpperCAmelCase_:		List[Any]							      =     processor.get_labels()
							if tokenizer.__class__ in (
							    RobertaTokenizer,
							    RobertaTokenizerFast,
							    XLMRobertaTokenizer,
							    BartTokenizer,
							    BartTokenizerFast,
							):
										# HACK(label indices are swapped in RoBERTa pretrained model)
										UpperCAmelCase_      ,							UpperCAmelCase_:		List[str]							      =     label_list[2], label_list[1]
							UpperCAmelCase_:		Dict							      =     label_list

							UpperCAmelCase_:		List[str]							      =     processor.get_dev_examples(SCREAMING_SNAKE_CASE_					) if evaluate else processor.get_train_examples(SCREAMING_SNAKE_CASE_					)
							UpperCAmelCase_:		Any							      =     hans_convert_examples_to_features(SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_					)

							def gen():
										for ex_index, ex in tqdm.tqdm(enumerate(self.features					),   desc="""convert examples to features"""					):
													if ex_index % 10000 == 0:
																logger.info("""Writing example %d of %d""" % (ex_index, len(SCREAMING_SNAKE_CASE_					))					)

													yield (
													    {
													        "example_id": 0,
													        "input_ids": ex.input_ids,
													        "attention_mask": ex.attention_mask,
													        "token_type_ids": ex.token_type_ids,
													    },
													    ex.label,
													)

							UpperCAmelCase_:		List[str]							      =     tf.data.Dataset.from_generator(
							    SCREAMING_SNAKE_CASE_,   (
							        {
							            """example_id""": tf.intaa,
							            """input_ids""": tf.intaa,
							            """attention_mask""": tf.intaa,
							            """token_type_ids""": tf.intaa,
							        },
							        tf.intaa,
							    ),   (
							        {
							            """example_id""": tf.TensorShape([]					),
							            """input_ids""": tf.TensorShape([None, None]					),
							            """attention_mask""": tf.TensorShape([None, None]					),
							            """token_type_ids""": tf.TensorShape([None, None]					),
							        },
							        tf.TensorShape([]					),
							    ),   )

				def      __snake_case       (self					)       ->   Optional[Any]:
							return self.dataset

				def __len__(self					)       ->   Optional[int]:
							return len(self.features					)

				def __getitem__(self,   SCREAMING_SNAKE_CASE_					)       ->   InputFeatures:
							return self.features[i]

				def      __snake_case       (self					)       ->   Union[str, Any]:
							return self.label_list







class 				_a				(   _lowerCAmelCase					):

			def      __snake_case       (self,   SCREAMING_SNAKE_CASE_					)       ->   Dict:
						return self._create_examples(self._read_tsv(os.path.join(SCREAMING_SNAKE_CASE_,   """heuristics_train_set.txt"""					)					),   """train"""					)

			def      __snake_case       (self,   SCREAMING_SNAKE_CASE_					)       ->   Dict:
						return self._create_examples(self._read_tsv(os.path.join(SCREAMING_SNAKE_CASE_,   """heuristics_evaluation_set.txt"""					)					),   """dev"""					)

			def      __snake_case       (self					)       ->   Optional[int]:
						return ["contradiction", "entailment", "neutral"]

			def      __snake_case       (self,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_					)       ->   List[Any]:
						UpperCAmelCase_:		Optional[Any]							      =     []
						for i, line in enumerate(SCREAMING_SNAKE_CASE_					):
									if i == 0:
												continue
									UpperCAmelCase_:		Tuple							      =     """%s-%s""" % (set_type, line[0])
									UpperCAmelCase_:		int							      =     line[5]
									UpperCAmelCase_:		int							      =     line[6]
									UpperCAmelCase_:		List[str]							      =     line[7][2:] if line[7].startswith("""ex"""					) else line[7]
									UpperCAmelCase_:		Any							      =     line[0]
									examples.append(InputExample(guid=SCREAMING_SNAKE_CASE_,   text_a=SCREAMING_SNAKE_CASE_,   text_b=SCREAMING_SNAKE_CASE_,   label=SCREAMING_SNAKE_CASE_,   pairID=SCREAMING_SNAKE_CASE_					)					)
						return examples







def  lowerCAmelCase_	(lowerCAmelCase__:		List[InputExample]		,						lowerCAmelCase__:		List[str]		,						lowerCAmelCase__:		int		,						lowerCAmelCase__:		PreTrainedTokenizer		,						):





			"""simple docstring"""


			UpperCAmelCase_:		List[str]							      =     {label: i for i, label in enumerate(lowerCAmelCase__  )}

			UpperCAmelCase_:		Optional[int]							      =     []
			for ex_index, example in tqdm.tqdm(enumerate(lowerCAmelCase__  )		,						desc="""convert examples to features"""  ):
						if ex_index % 1_0_0_0_0 == 0:
									logger.info("""Writing example %d""" % (ex_index)  )

						UpperCAmelCase_:		Tuple							      =     tokenizer(
						    example.text_a		,						example.text_b		,						add_special_tokens=lowerCAmelCase__		,						max_length=lowerCAmelCase__		,						padding="""max_length"""		,						truncation=lowerCAmelCase__		,						return_overflowing_tokens=lowerCAmelCase__		,						)

						UpperCAmelCase_:		Optional[Any]							      =     label_map[example.label] if example.label in label_map else 0

						UpperCAmelCase_:		Tuple							      =     int(example.pairID  )

						features.append(InputFeatures(**lowerCAmelCase__		,						label=lowerCAmelCase__		,						pairID=lowerCAmelCase__  )  )

			for i, example in enumerate(examples[:5]  ):
						logger.info("""*** Example ***"""  )
						logger.info(F'guid: {example}'  )
						logger.info(F'features: {features[i]}'  )

			return features


a      :  Optional[int]			     =			{
    'hans': 3,
}

a      :  Dict			     =			{
    'hans': HansProcessor,
}






